发表机构
CEIMIA; Carleton University; University of Ottawa（CEIMIA; 卡尔顿大学; 渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文分析74项国家及3项区域AI战略，发现国家AI战略在经济竞争力等方面横向趋同，在人权目标等方面分化，不同区域纵向趋同情况各异，为AI政策设计提供依据。
AI 中文摘要
全球各国针对人工智能的快速扩张，纷纷发布国家与区域层面的AI战略。对比国家与区域AI战略以识别其趋同与分化，可揭示共同实践、理解区域差异，并为政策制定者提供一套全面的政策设计要素，用于正在推进的AI战略开发。然而，现有研究尚未考察这些战略的潜在政策设计要素，也未评估这些要素在横向（国与国之间）或纵向（区域与国家之间）层面是否随时间呈现趋同或分化。本文通过对从全球205个联合国成员国及非成员国中筛选出的74项国家AI战略和3项区域AI战略进行编码与分析，填补了这一研究空白。编码采用潜在归纳法，围绕三大功能性政策设计要素组织：目标、路径与原则。分析由两个研究问题引导：国家AI战略随时间在横向层面的趋同或分化程度如何；国家战略与本国区域AI战略在纵向层面的趋同或分化程度如何。结果显示，在经济竞争力、研究支持以及AI的伦理应用方面存在强烈的横向趋同，而在人权目标、参与式治理路径以及以人为本的原则方面则持续分化。在三大区域中，非洲联盟（AU）表现出最高的纵向趋同性；欧盟（EU）在监管与经济优先事项上展现出高度一致性，但在以人为本的价值观上存在分化；北欧-波罗的海区域则呈现出混合的纵向趋同态势。这些发现为政策制定者提供了全面的证据基础，助力其在开发和更新AI战略时识别新兴的AI政策设计选择规范。
英文摘要
Governments worldwide have responded to the rapid expansion of AI by publishing national and regional AI strategies. Comparing national and regional AI strategies to identify their convergences and divergences can uncover their common practices, understand regional variations, and provide policy designers a comprehensive set of policy design elements for their ongoing AI strategy developments. Yet, existing work has not examined their underlying policy design elements or assessed whether those elements are horizontally (country-to-country) or vertical (region-to-country) converging or diverging over time. This paper addresses that gap by coding and analyzing 74 national and 3 regional AI strategies drawn from a global scan of all 205 UN member and non-member states. The coding used a latent-inductive approach organized around three functional policy design elements: goals, approaches, and principles. Two research questions guided the analysis: to what degree are national AI strategies becoming horizontally convergent or divergent over time; and to what degree are national strategies becoming vertically convergent or divergent with those countries' regional AI strategy. Results indicate strong horizontal convergence around economic competitiveness, research support, and ethical AI use, alongside persistent divergence in human rights goals, participatory governance approaches, and human-centric principles. Across the three regions, the AU exhibits the highest vertical convergence, the EU demonstrated strong alignment on regulatory and economic priorities but diverges on human-centric values, and the Nordic-Baltic Region displays mixed vertical convergence. These findings offer policy designers a comprehensive evidence base for identifying emerging AI policy design choice norms as AI strategies are developed and updated.
